There are a lot of claims and assumptions in these ten tweets.
I've frequently made similar claims (hence Titus's wariness),
but after working with scientists daily for six years,
I'm less sure of myself.
Are today's tools and notations for computational science actually inadequate?
Do less than 5% of scientists use the tools we have?
Do better tools actually generate more users?
Is Git really better than CVS or Subversion?
People who do empirical studies of software engineers would say,
"We don't know how to measure that,"
"We don't know,"
"Unproven,"
and, "That study hasn't been done, but probably not for most users" respectively.

The fact that those questions popped into my head
has made me realize that I might finally be an engineer.
Consider:

I want the Python and Julia communities to user-test features
à la Stefik et al
before adding them to the language

I want Software Carpentry and Data Carpentry to do more
assessment
in 2016 to find out what's effective and where we can make improvements.

I'm going to work full-time on instructor training
for the next twelve months
because I believe that if we apply educational research in the classroom,
teaching and learning will both be improved.

What ties these together is the belief that if we start with,
"I don't know, but I can find out",
we can make our world better.
That—the use of the scientific method to improve the universe instead of merely understanding it—is
as good a definition of engineering as I know.
Among all the thoughts
prompted by this
difficultyear,
the discovery that I might finally be thinking like an engineer
three decades after earning a degree in the subject
is oddly comforting.